一种用户画像系统的设计与实现

摘要

用户画像系统通过结合用户浏览行为日志以及爬取数据作为补充,构成用户浏览行为的完整数据集.通过以Hadoop分布式集群为基础的大数据平台结合数据分析算法对该数据集进行清洗、规范化、分析与处理,分析出用户兴趣偏好,为用户标记不同权重的标签,使得企业更了解用户以及为之后针对用户精准推荐铺平道路.此外,针对K-means算法依赖初始化中心的缺陷进行了改进,从测试结果可以看出改进后的K-means准确率得到了较大提升.%The user profile system constructed complete data set of users browsing behaviour by combing user browsing log and crawling data.The data set is cleaned,normalized,analysed,and processed by data analysis algorithm and a big data platform based on Hadoop distributed cluster to get interests of users and mark labels of different weights for users, which enables enterprises to understand users and paves the way of precise recommendation for users.In addition,the defect of K-means algorithm relying on the initialization centre is improved, and the improved K-means algorithm has higher accuracy.